The overall goal of this proposal is to develop quantitative models that describe how prior immunity affects the dynamics and evolution of recall immune responses to influenza viruses. Our goal is to understand the fundamental rules underlying the dynamics of virus and immunity in pathogens that exhibit strain variation, and address the following questions: Why do responses to conserved epitopes not get boosted with each new influenza strain and generate strain-transcending immunity? What is the role of CD8 T cells during recall response, what is the nature of the protection from infection and pathology different cell subsets provide, and why do influenza-specific T cell epitopes show little variation? We will answer these questions through three Specific Aims. In Aim 1, we will build and test compartmental mechanistic models to understand how prior humoral immunity affects recall response to different hemagglutinin molecules of influenza strains. We will develop multi-epitope models that track the dynamics of clones of B cells, plasma cells, antibodies and CD4 T cell help to different virus epitopes on the HA molecule. Our models describe how epitope masking by secreted antibodies raised against previous strains plays a key role in understanding competition between responses to different epitopes. In Aim 2, we will build and test models to understand how prior CD8 T cell immunity affects recall response to influenza. We will develop multi-epitope models for CD8 T cell responses and use them to identify the key parameters that regulate proliferation, competition and differentiation of CD8 T cells. For Aim 3, we will combine the models from Aims 1 and 2 to predict how the combined effect of preexisting humoral and T cell immunity affects the diversity and evolution of preexisting and stimulation of new clones of immune cells following sequential challenge with different influenza strains and vaccines. We will also develop models based on phylodynamic approaches to analyze next-generation sequences of the antigen receptors on virus-specific T and B cells. All our models will be validated through experiments that will be done concurrently in Project 2 of this application. Finally, Aim 4 of this project is devoted to the development and dissemination of user-friendly and powerful modeling tools that can be used by the wider research community for immunological modeling. We will design and write a new R package that allows graphical model building and analysis. We will also develop several tools for sequence analysis based on the BEAST and Galaxy platforms. By tapping into the infrastructure of existing, widely used modeling tools, we will be able to produce tools that are at the same time very user friendly and highly flexible.